Method and apparatus for interframe point cloud attribute coding

ABSTRACT

A method of interframe point cloud attribute coding is performed by at least one processor and includes obtaining, as a motion estimation unreliability measure of motion estimation of a target frame, a value inversely proportional to a ratio of a number of first point cloud samples of the target frame respectively with second point cloud samples of an interframe reference frame, to a number of point cloud samples in the target frame. The method further includes identifying whether the obtained motion estimation unreliability measure is greater than a predetermined threshold, based on the obtained motion estimation unreliability measure being identified to be greater than the predetermined threshold, skipping motion compensation of the target frame, and based on the obtained motion estimation unreliability measure being identified to be less than or equal to the predetermined threshold, performing the motion compensation of the target frame.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from U.S. Provisional PatentApplication No. 62/822,765, filed on Mar. 22, 2019, in the U.S. Patentand Trademark Office, which is incorporated herein by reference in itsentirety.

BACKGROUND 1. Field

Methods and apparatuses consistent with embodiments relate tograph-based point cloud compression (G-PCC), and more particularly, amethod and an apparatus for interframe point cloud attribute coding.

2. Description of Related Art

Advanced three-dimensional (3D) representations of the world areenabling more immersive forms of interaction and communication, and alsoallow machines to understand, interpret and navigate our world. 3D pointclouds have emerged as an enabling representation of such information. Anumber of use cases associated with point cloud data have beenidentified, and corresponding requirements for point cloudrepresentation and compression have been developed.

A point cloud is a set of points in a 3D space, each with associatedattributes, e.g., color, material properties, etc. Point clouds can beused to reconstruct an object or a scene as a composition of suchpoints. They can be captured using multiple cameras and depth sensors invarious setups, and may be made up of thousands up to billions of pointsto realistically represent reconstructed scenes.

Compression technologies are needed to reduce the amount of data torepresent a point cloud. As such, technologies are needed for lossycompression of point clouds for use in real-time communications and sixdegrees of freedom (6DoF) virtual reality. In addition, technology issought for lossless point cloud compression in the context of dynamicmapping for autonomous driving and cultural heritage applications, etc.The Moving Picture Experts Group (MPEG) has started working on astandard to address compression of geometry and attributes such ascolors and reflectance, scalable/progressive coding, coding of sequencesof point clouds captured over time, and random access to subsets of apoint cloud.

FIG. 1A is a diagram illustrating a method of generating levels ofdetail (LoD) in G-PCC.

Referring to FIG. 1A, in current G-PCC attributes coding, an LoD (i.e.,a group) of each 3D point (e.g., P0-P9) is generated based on a distanceof each 3D point, and then attribute values of 3D points in each LoD isencoded by applying prediction in an LoD-based order 110 instead of anoriginal order 105 of the 3D points. For example, an attributes value ofthe 3D point P2 is predicted by calculating a distance-based weightedaverage value of the 3D points P0, P5 and P4 that were encoded ordecoded prior to the 3D point P2.

A current anchor method in G-PCC proceeds as follows.

First, a variability of a neighborhood of a 3D point is computed tocheck how different neighbor values are, and if the variability is lowerthan a threshold, the calculation of the distance-based weighted averageprediction is conducted by predicting attribute values(a_(i))_(i∈0 . . . k−1), using a linear interpolation process based ondistances of nearest neighbors of a current point i. Let

_(i) be a set of k-nearest neighbors of the current point i, let

be their decoded/reconstructed attribute values and let

be their distances to the current point i. A predicted attribute valueâ_(i) is then given by:

$\begin{matrix}{{\hat{a}}_{i} = {{{Round}\ ( {\frac{1}{k}{\sum\limits_{j \in \aleph_{i}}^{\;}{\frac{\frac{1}{\delta_{j}^{2}}}{\sum\limits_{j \in \aleph_{i}}^{\;}\frac{1}{\delta_{j}^{2}}}{\overset{\sim}{a}}_{j}}}} )}.}} & ( {{Eq}.\mspace{14mu} 1} )\end{matrix}$

Note that geometric locations of all point clouds are already availablewhen attributes are coded. In addition, the neighboring points togetherwith their reconstructed attribute values are available both at anencoder and a decoder as a k-dimensional tree structure that is used tofacilitate a nearest neighbor search for each point in an identicalmanner.

Second, if the variability is higher than the threshold, arate-distortion optimized (RDO) predictor selection is performed.Multiple predictor candidates or candidate predicted values are createdbased on a result of a neighbor point search in generating LoD. Forexample, when the attributes value of the 3D point P2 is encoded byusing prediction, a weighted average value of distances from the 3Dpoint P2 to respectively the 3D points P0, P5 and P4 is set to apredictor index equal to 0. Then, a distance from the 3D point P2 to thenearest neighbor point P4 is set to a predictor index equal to 1.Moreover, distances from the 3D point P2 to respectively the nextnearest neighbor points P5 and P0 are set to predictor indices equal to2 and 3, as shown in Table 1 below.

TABLE 1 Sample of predictor candidate for attributes coding Predictorindex Predicted value 0 average 1 P4 (1^(st) nearest point) 2 P5 (2^(nd)nearest point) 3 P0 (3^(rd) nearest point)

After creating predictor candidates, a best predictor is selected byapplying a rate-distortion optimization procedure, and then, a selectedpredictor index is mapped to a truncated unary (TU) code, bins of whichwill be arithmetically encoded. Note that a shorter TU code will beassigned to a smaller predictor index in Table 1.

A maximum number of predictor candidates MaxNumCand is defined and isencoded into an attributes header. In the current implementation, themaximum number of predictor candidates MaxNumCand is set to equal tonumberOfNearestNeighborsInPrediction+1 and is used in encoding anddecoding predictor indices with a truncated unary binarization.

A lifting transform for attribute coding in G-PCC builds on top of apredicting transform described above. A main difference between theprediction scheme and the lifting scheme is the introduction of anupdate operator.

FIG. 1B is a diagram of an architecture for P/U(Prediction/Update)-lifting in G-PCC. To facilitate prediction andupdate steps in lifting, one has to split a signal into two sets ofhigh-correlation at each stage of decomposition. In the lifting schemein G-PCC, the splitting is performed by leveraging an LoD structure inwhich such high-correlation is expected among levels and each level isconstructed by a nearest neighbor search to organize non-uniform pointclouds into a structured data. A P/U decomposition step at a level Nresults in a detail signal D(N−1) and an approximation signal A(N−1),which is further decomposed into D(N−2) and A(N−2). This step isrepeatedly applied until a base layer approximation signal A(1) isobtained.

Consequently, instead of coding an input attribute signal itself thatconsists of LOD(N), . . . , LOD(1), one ends up coding D(N−1), D(N−2), .. . , D(1), A(1) in the lifting scheme. Note that application ofefficient P/U steps often leads to sparse sub-bands “coefficients” inD(N−1), . . . , D(1), thereby providing a transform coding gainadvantage.

Currently, a distance-based weighted average prediction described abovefor the predicting transform is used for a prediction step in thelifting as an anchor method in G-PCC.

In prediction and lifting for attribute coding in G-PCC, an availabilityof neighboring attribute samples is important for compression efficiencyas more of the neighboring attribute samples can provide betterprediction. In a case in which there are not enough neighbors to predictfrom, the compression efficiency can be compromised.

SUMMARY

According to embodiments, a method of interframe point cloud attributecoding is performed by at least one processor and includes obtaining, asa motion estimation unreliability measure of motion estimation of atarget frame, a value inversely proportional to a ratio of a number offirst point cloud samples of the target frame respectively with secondpoint cloud samples of an interframe reference frame, to a number ofpoint cloud samples in the target frame. The method further includesidentifying whether the obtained motion estimation unreliability measureis greater than a predetermined threshold, based on the obtained motionestimation unreliability measure being identified to be greater than thepredetermined threshold, skipping motion compensation of the targetframe, and based on the obtained motion estimation unreliability measurebeing identified to be less than or equal to the predeterminedthreshold, performing the motion compensation of the target frame.

According to embodiments, an apparatus for interframe point cloudattribute coding includes at least one memory configured to storecomputer program code, and at least one processor configured to accessthe at least one memory and operate according to the computer programcode. The computer program code includes obtaining code configured tocause the at least one processor to obtain, as a motion estimationunreliability measure of motion estimation of a target frame, a valueinversely proportional to a ratio of a number of first point cloudsamples of the target frame respectively with second point cloud samplesof an interframe reference frame, to a number of point cloud samples inthe target frame. The computer program code further includes identifyingcode configured to cause the at least one processor to identify whetherthe obtained motion estimation unreliability measure is greater than apredetermined threshold, skipping code configured to cause the at leastone processor to, based on the obtained motion estimation unreliabilitymeasure being identified to be greater than the predetermined threshold,skip motion compensation of the target frame, and performing codeconfigured to cause the at least one processor to, based on the obtainedmotion estimation unreliability measure being identified to be less thanor equal to the predetermined threshold, perform the motion compensationof the target frame.

A non-transitory computer-readable storage medium storing instructionsthat cause at least one processor to obtain, as a motion estimationunreliability measure of motion estimation of a target frame, a valueinversely proportional to a ratio of a number of first point cloudsamples of the target frame respectively with second point cloud samplesof an interframe reference frame, to a number of point cloud samples inthe target frame. The instructions further cause the at least oneprocessor to identify whether the obtained motion estimationunreliability measure is greater than a predetermined threshold, basedon the obtained motion estimation unreliability measure being identifiedto be greater than the predetermined threshold, skip motion compensationof the target frame, and based on the obtained motion estimationunreliability measure being identified to be less than or equal to thepredetermined threshold, perform the motion compensation of the targetframe.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a diagram illustrating a method of generating LoD in G-PCC.

FIG. 1B is a diagram of an architecture for P/U-lifting in G-PCC.

FIG. 2 is a block diagram of a communication system according toembodiments.

FIG. 3 is a diagram of a placement of a G-PCC compressor and a G-PCCdecompressor in an environment, according to embodiments.

FIG. 4 is a functional block diagram of the G-PCC compressor accordingto embodiments.

FIG. 5 is a functional block diagram of the G-PCC decompressor accordingto embodiments.

FIG. 6 is a flowchart illustrating a method of interframe point cloudattribute coding, according to embodiments.

FIG. 7 is a block diagram of an apparatus for interframe point cloudattribute coding, according to embodiments.

FIG. 8 is a diagram of a computer system suitable for implementingembodiments.

DETAILED DESCRIPTION

Embodiments described herein provide a method and an apparatus forinterframe point cloud attribute coding. In detail, an attribute valuefrom other point cloud frames at different time instances is used inaddition to an attribute value from within the same point cloud framefor prediction in G-PCC. The method and the apparatus can be used toimprove prediction in Differential Pulse Code Modulation (DPCM) (aka apredicting transform) or a predict step for lifting (aka a liftingtransform) in G-PCC. The method and the apparatus of spatio-temporalprediction can also work for any codecs with a similar structure. Themethod and the apparatus can improve prediction performance especiallywhen point-cloud samples are sparse within a current frame by providingsample attribute values from corresponding locations in other frames.

FIG. 2 is a block diagram of a communication system 200 according toembodiments. The communication system 200 may include at least twoterminals 210 and 220 interconnected via a network 250. Forunidirectional transmission of data, a first terminal 210 may code pointcloud data at a local location for transmission to a second terminal 220via the network 250. The second terminal 220 may receive the coded pointcloud data of the first terminal 210 from the network 250, decode thecoded point cloud data and display the decoded point cloud data.Unidirectional data transmission may be common in media servingapplications and the like.

FIG. 2 further illustrates a second pair of terminals 230 and 240provided to support bidirectional transmission of coded point cloud datathat may occur, for example, during videoconferencing. For bidirectionaltransmission of data, each terminal 230 or 240 may code point cloud datacaptured at a local location for transmission to the other terminal viathe network 250. Each terminal 230 or 240 also may receive the codedpoint cloud data transmitted by the other terminal, may decode the codedpoint cloud data and may display the decoded point cloud data at a localdisplay device.

In FIG. 2, the terminals 210-240 may be illustrated as servers, personalcomputers and smartphones, but principles of the embodiments are not solimited. The embodiments find application with laptop computers, tabletcomputers, media players and/or dedicated video conferencing equipment.The network 250 represents any number of networks that convey codedpoint cloud data among the terminals 210-240, including for examplewireline and/or wireless communication networks. The communicationnetwork 250 may exchange data in circuit-switched and/or packet-switchedchannels. Representative networks include telecommunications networks,local area networks, wide area networks and/or the Internet. For thepurposes of the present discussion, an architecture and topology of thenetwork 250 may be immaterial to an operation of the embodiments unlessexplained herein below.

FIG. 3 is a diagram of a placement of a G-PCC compressor 303 and a G-PCCdecompressor 310 in an environment, according to embodiments. Thedisclosed subject matter can be equally applicable to other point cloudenabled applications, including, for example, video conferencing,digital TV, storing of compressed point cloud data on digital mediaincluding CD, DVD, memory stick and the like, and so on.

A streaming system 300 may include a capture subsystem 313 that caninclude a point cloud source 301, for example a digital camera,creating, for example, uncompressed point cloud data 302. The pointcloud data 302 having a higher data volume can be processed by the G-PCCcompressor 303 coupled to the point cloud source 301. The G-PCCcompressor 303 can include hardware, software, or a combination thereofto enable or implement aspects of the disclosed subject matter asdescribed in more detail below. Encoded point cloud data 304 having alower data volume can be stored on a streaming server 305 for futureuse. One or more streaming clients 306 and 308 can access the streamingserver 305 to retrieve copies 307 and 309 of the encoded point clouddata 304. A client 306 can include the G-PCC decompressor 310, whichdecodes an incoming copy 307 of the encoded point cloud data and createsoutgoing point cloud data 311 that can be rendered on a display 312 orother rendering devices (not depicted). In some streaming systems, theencoded point cloud data 304, 307 and 309 can be encoded according tovideo coding/compression standards. Examples of those standards includethose being developed by MPEG for G-PCC.

FIG. 4 is a functional block diagram of a G-PCC compressor 303 accordingto embodiments.

As shown in FIG. 4, the G-PCC compressor 303 includes a quantizer 405, apoints removal module 410, an octree encoder 415, an attributes transfermodule 420, an LoD generator 425, a prediction module 430, a quantizer435 and an arithmetic coder 440.

The quantizer 405 receives positions of points in an input point cloud.The positions may be (x,y,z)-coordinates. The quantizer 405 furtherquantizes the received positions, using, e.g., a scaling algorithmand/or a shifting algorithm.

The points removal module 410 receives the quantized positions from thequantizer 405, and removes or filters duplicate positions from thereceived quantized positions.

The octree encoder 415 receives the filtered positions from the pointsremoval module 410, and encodes the received filtered positions intooccupancy symbols of an octree representing the input point cloud, usingan octree encoding algorithm. A bounding box of the input point cloudcorresponding to the octree may be any 3D shape, e.g., a cube.

The octree encoder 415 further reorders the received filtered positions,based on the encoding of the filtered positions.

The attributes transfer module 420 receives attributes of points in theinput point cloud. The attributes may include, e.g., a color or RGBvalue and/or a reflectance of each point. The attributes transfer module420 further receives the reordered positions from the octree encoder415.

The attributes transfer module 420 further updates the receivedattributes, based on the received reordered positions. For example, theattributes transfer module 420 may perform one or more amongpre-processing algorithms on the received attributes, the pre-processingalgorithms including, for example, weighting and averaging the receivedattributes and interpolation of additional attributes from the receivedattributes. The attributes transfer module 420 further transfers theupdated attributes to the prediction module 430.

The LoD generator 425 receives the reordered positions from the octreeencoder 415, and obtains an LoD of each of the points corresponding tothe received reordered positions. Each LoD may be considered to be agroup of the points, and may be obtained based on a distance of each ofthe points. For example, as shown in FIG. 1A, points P0, P5, P4 and P2may be in an LoD LOD0, points P0, P5, P4, P2, P1, P6 and P3 may be in anLoD LOD1, and points P0, P5, P4, P2, P1, P6, P3, P9, P8 and P7 may be inan LoD LOD2.

The prediction module 430 receives the transferred attributes from theattributes transfer module 420, and receives the obtained LoD of each ofthe points from the LoD generator 425. The prediction module 430 obtainsprediction residuals (values) respectively of the received attributes byapplying a prediction algorithm to the received attributes in an orderbased on the received LoD of each of the points. The predictionalgorithm may include any among various prediction algorithms such as,e.g., interpolation, weighted average calculation, a nearest neighboralgorithm and RDO.

For example, as shown in FIG. 1A, the prediction residuals respectivelyof the received attributes of the points P0, P5, P4 and P2 included inthe LoD LOD0 may be obtained first prior to those of the receivedattributes of the points P1, P6, P3, P9, P8 and P7 included respectivelyin the LoDs LOD1 and LOD2. The prediction residuals of the receivedattributes of the point P2 may be obtained by calculating a distancebased on a weighted average of the points P0, P5 and P4.

The quantizer 435 receives the obtained prediction residuals from theprediction module 430, and quantizes the received predicted residuals,using, e.g., a scaling algorithm and/or a shifting algorithm.

The arithmetic coder 440 receives the occupancy symbols from the octreeencoder 415, and receives the quantized prediction residuals from thequantizer 435. The arithmetic coder 440 performs arithmetic coding onthe received occupancy symbols and quantized predictions residuals toobtain a compressed bitstream. The arithmetic coding may include anyamong various entropy encoding algorithms such as, e.g.,context-adaptive binary arithmetic coding.

FIG. 5 is a functional block diagram of a G-PCC decompressor 310according to embodiments.

As shown in FIG. 5, the G-PCC decompressor 310 includes an arithmeticdecoder 505, an octree decoder 510, an inverse quantizer 515, an LoDgenerator 520, an inverse quantizer 525 and an inverse prediction module530.

The arithmetic decoder 505 receives the compressed bitstream from theG-PCC compressor 303, and performs arithmetic decoding on the receivedcompressed bitstream to obtain the occupancy symbols and the quantizedprediction residuals. The arithmetic decoding may include any amongvarious entropy decoding algorithms such as, e.g., context-adaptivebinary arithmetic decoding.

The octree decoder 510 receives the obtained occupancy symbols from thearithmetic decoder 505, and decodes the received occupancy symbols intothe quantized positions, using an octree decoding algorithm.

The inverse quantizer 515 receives the quantized positions from theoctree decoder 510, and inverse quantizes the received quantizedpositions, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed positions of the points in the input pointcloud.

The LoD generator 520 receives the quantized positions from the octreedecoder 510, and obtains the LoD of each of the points corresponding tothe received quantized positions.

The inverse quantizer 525 receives the obtained quantized predictionresiduals, and inverse quantizes the received quantized predictionresiduals, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed prediction residuals.

The inverse prediction module 530 receives the obtained reconstructedprediction residuals from the inverse quantizer 525, and receives theobtained LoD of each of the points from the LoD generator 520. Theinverse prediction module 530 obtains reconstructed attributesrespectively of the received reconstructed prediction residuals byapplying a prediction algorithm to the received reconstructed predictionresiduals in an order based on the received LoD of each of the points.The prediction algorithm may include any among various predictionalgorithms such as, e.g., interpolation, weighted average calculation, anearest neighbor algorithm and RDO. The reconstructed attributes are ofthe points in the input point cloud.

The method and the apparatus for interframe point cloud attribute codingwill now be described in detail. Such a method and an apparatus may beimplemented in the G-PCC compressor 303 described above, namely, theprediction module 430. The method and the apparatus may also beimplemented in the G-PCC decompressor 310, namely, the inverseprediction module 530.

Motion Estimation and Compensation

In embodiments, a geometry-based or joint geometry/attribute-basedglobal/local motion estimation may be performed.

In detail, in the context of point cloud compression, geometryinformation such as positions of point clouds is available whenattribute coding is performed. This information can be leveraged orcombined to perform motion estimation to compensate for any local orglobal motion present in a current frame and reference frames.

Because performing motion estimation on sparse point cloud data can bedifficult or unreliable, a motion estimation unreliability measureme_uncertainty may be obtained as a result of the motion estimation. Themotion estimation unreliability measure can be based upon, as anexample, the number of candidate target samples with similar motionmatching scores, or a threshold test of such scores. Each of the motionmatching scores may be obtained through a process such as blockmatching.

The motion estimation unreliability measure me_uncertainty (when greaterthan a predetermined threshold) can disable/enable a use of interframeprediction or can be used when determining scaling or weighting factorsin prediction.

Modified Nearest neighbor Search

In embodiments, a prediction that employs nearest neighbor point cloudsas in G-PCC can consider neighbor samples from other frames asadditional candidates.

A G-PCC design generates LoD layers of point clouds as follows. First,an original point cloud and a reference point cloud are sorted using theMorton code.

Next, an original point cloud is sampled from a top of LoD layers downto a bottom LoD level, successively according to a sample distance.Then, a nearest neighbor search is performed for each point belonging toan LoD. A neighbor list is then built for each point cloud, in whichgeometrically closer samples come in an early part of the list.

In embodiments, the following provisions further facilitate the nearestneighbor list building with interframe clouds. A flag interframe isdefined and indicates whether a nearest neighbor sample is intra orinter. A variable framenum is defined and indicates a frame number oroffsets in Picture Order Count (POC) from a current point cloud frame. Amaximum number of interframe nearest neighbor samples MaxInterNN isdefined.

Further, whenever a new point cloud sample candidate is compared withthose already in the list, a notion of distance is defined. In thisintra/inter mixed prediction context, variables calledTemporal-to-Spatial Scale (TSScale) and TSOffset are introduced toreflect possible degrees of changes of attribute values between frames.If these values are large, a likelihood of attribute value changes ishigh due to a temporal distance, possible fast motion, scene changes,etc. In such cases, having similar 3D coordinates is less related to acloseness of attribute values.

In embodiments of the nearest neighbor search and its use in a laterstage of prediction, TSScale can be used to scale-up/down a relativeproximity of temporal versus spatial distance, while TSOffset can beused to add an offset to a 3D coordinate of a point in a reference framewhen hypothetically “merging” frames to choose among mixed intra andinter frame cloud samples. This “merging” treats interframe samples asif they were belonging to a current frame, in which case there could bepossibly multiple candidate prediction samples at an identical 3Dlocation.

In embodiments, options are added to reshuffle an order of nearestneighbor sample candidates. The encoder (the G-PCC compressor 303) cansignal, based upon a confidence on motion estimation (i.e.,me_uncertainty), a way a candidate list is structured or that a temporaldimension distance is reflected in a weighted average or a weightcalculation of an interframe candidate point cloud sample.

In embodiments, a method of calculating a motion estimationunreliability measure me_uncertainty includes calculating its inverselyproportional value, such as the ratio of the number of current targetframe point cloud samples with (i.e., matching or corresponding to) oneor more interframe reference point cloud samples from other frames tothe number of point cloud samples in a current target frame. The one ormore interframe reference point cloud samples from other frames arefound by a nearest neighbor search as described above with the proposedmodifications for interframe point clouds. The smaller the ratio is, themore uncertain a motion estimation is. This is because, as an outputfrom the proposed modified nearest neighbor search, temporal predictioncandidate points as per a spatio-temporal distance metric as describedin this disclosure will be included in a nearest neighbor list. Havingmany such candidate points in the list leads to a large ratio number andimplies a current target frame and reference frames are reasonably in agood match either by explicit motion compensation or a static motionlessnature of a scene and objects.

Based upon the motion estimation unreliability measure being greaterthan a predetermined threshold, as an example, the encoder and a decoder(the G-PCC decompressor 310) can agree to skip the motion compensationfor a current or target frame. This process is possible as the modifiednearest neighborhood search is conducted both at the encoder and thedecoder. Hence, there is no extra required overhead signaling. This istrue for G-PCC in particular. This process can designate parts of entirepoint cloud data in an adaptive manner in which the motion compensationis performed prior to prediction/transform.

Applications to G-PCC Predicting Transform for Attributes

1. RDO Index-Coding

The above embodiments can be applied to an RDO-based predictor selectionas described above. In detail, a provision of assigning a higherpriority to temporal candidates (interframe=1) is provided subject tocertain conditions. In embodiments, when a motion estimationunreliability measure me_uncertainty is low, the higher priority isassigned to a temporal candidate by putting it early in the nearestneighbor list and vice versa. This includes excluding a temporalcandidate from the nearest neighbor list when the motion estimationunreliability measure me_uncertainty is above a predetermined threshold.When there are multiple interframes, a candidate point cloud sample witha closer temporal distance (as indicated by framenum) is placed earlierin the list.

An RDO encoder (the G-PCC compressor 303) and the decoder (the G-PCCdecompressor 310) can keep the track of an interframe choice and makeadaptive index-order switching in a synchronous manner. Further, anumber of interframe candidates MaxInterNN may be adaptively changeddepending upon the above conditions.

2. Distance-Based Average Prediction

The above embodiments can be applied to a distance weighted averageprediction as described above. In detail, when a motion estimationunreliability measure me_uncertainty is high, an interframe candidate isnot included in a weighted average.

In embodiments, a distance-weighted average prediction a of an attributevalue of a current point cloud is defined using inter and intra nearestneighbor sample values a_(n)′s (n=1, . . . , N) as follows:

ā=Σ _(n=1) ^(N) W _(n) a _(n).  (Eq. 2)

Here a weight of an n-th sample can be determined as follows:

$\begin{matrix}{{W_{n} \propto ( \frac{1}{( {{TSScale} \times {{p_{n} + {TSOffset} - p}}} )} )},} & ( {{Eq}.\mspace{14mu} 3} )\end{matrix}$

where p is a position of the current point cloud sample with anattribute a, and p_(n) is a position of an n-th neighbor sample with acorresponding attribute value a_(n). Parameters TSScale and TSOffset areassigned as per the description above for interframe nearest neighbors.For intra nearest neighbors, TSScale is set 1 and TSOffset is set to 0.

FIG. 6 is a flowchart illustrating a method 600 of interframe pointcloud attribute coding, according to embodiments. In someimplementations, one or more process blocks of FIG. 6 may be performedby the G-PCC decompressor 310. In some implementations, one or moreprocess blocks of FIG. 6 may be performed by another device or a groupof devices separate from or including the G-PCC decompressor 310, suchas the G-PCC compressor 303.

Referring to FIG. 6, in a first block 610, the method 600 includesobtaining, as a motion estimation unreliability measure of motionestimation of a target frame, a value inversely proportional to a ratioof a number of first point cloud samples of the target framerespectively with second point cloud samples of an interframe referenceframe, to a number of point cloud samples in the target frame.

In a second block 620, the method 600 includes identifying whether theobtained motion estimation unreliability measure is greater than apredetermined threshold.

In a third block 630, the method 600 includes, based on the obtainedmotion estimation unreliability measure being identified to be greaterthan the predetermined threshold, skipping motion compensation of thetarget frame.

In a fourth block 640, the method 600 includes, based on the obtainedmotion estimation unreliability measure being identified to be less thanor equal to the predetermined threshold, performing the motioncompensation of the target frame.

A lesser value of the motion estimation unreliability measure mayindicate a greater uncertainty of the motion estimation, and a greatervalue of the motion estimation unreliability measure may indicate alesser uncertainty of the motion estimation.

The method may further include obtaining the second point cloud samples,using a nearest-neighbor search algorithm.

The method may further include, based on the obtained motion estimationunreliability measure being identified to be greater than thepredetermined threshold, skipping prediction of an attribute of a pointincluded in one among the point cloud samples in the target frame.

The method may further include performing prediction of an attribute ofa point included in one among the point cloud samples in the targetframe of which the motion compensation is performed.

The attribute may include either one or both of a color value and areflectance value of the point.

The performing the prediction may include applying a predictingtransform or a lifting transform to the attribute.

Although FIG. 6 shows example blocks of the method 600, in someimplementations, the method 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of the method 600 may be performed in parallel.

Further, the proposed methods may be implemented by processing circuitry(e.g., one or more processors or one or more integrated circuits). In anexample, the one or more processors execute a program that is stored ina non-transitory computer-readable medium to perform one or more of theproposed methods.

FIG. 7 is a block diagram of an apparatus 700 for interframe point cloudattribute coding, according to embodiments.

Referring to FIG. 7, the apparatus 700 includes obtaining code 710,identifying code 720, skipping code 730 and performing code 740.

The obtaining code 710 is configured to cause at least one processor toobtain, as a motion estimation unreliability measure of motionestimation of a target frame, a value inversely proportional to a ratioof a number of first point cloud samples of the target framerespectively with second point cloud samples of an interframe referenceframe, to a number of point cloud samples in the target frame;

The identifying code 720 is configured to cause the at least oneprocessor to identify whether the obtained motion estimationunreliability measure is greater than a predetermined threshold.

The skipping code 730 is configured to cause the at least one processorto, based on the obtained motion estimation unreliability measure beingidentified to be greater than the predetermined threshold, skip motioncompensation of the target frame.

The performing code 740 is configured to cause the at least oneprocessor to, based on the obtained motion estimation unreliabilitymeasure being identified to be less than or equal to the predeterminedthreshold, perform the motion compensation of the target frame.

A lesser value of the motion estimation unreliability measure mayindicate a greater uncertainty of the motion estimation, and a greatervalue of the motion estimation unreliability measure may indicate alesser uncertainty of the motion estimation.

The obtaining code 710 may be further configured to cause the at leastone processor to obtain the second point cloud samples, using anearest-neighbor search algorithm.

The skipping code 730 may be further configured to cause the at leastone processor to, based on the obtained motion estimation unreliabilitymeasure being identified to be greater than the predetermined threshold,skip prediction of an attribute of a point included in one among thepoint cloud samples in the target frame.

The performing code 740 may be further configured to cause the at leastone processor to perform prediction of an attribute of a point includedin one among the point cloud samples in the target frame of which themotion compensation is performed.

The attribute may include either one or both of a color value and areflectance value of the point.

The performing code 740 may be further configured to cause the at leastone processor to apply a predicting transform or a lifting transform tothe attribute.

FIG. 8 is a diagram of a computer system 800 suitable for implementingembodiments.

Computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code including instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by computer central processing units (CPUs),Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 8 for the computer system 800 are examplesin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementing theembodiments. Neither should the configuration of the components beinterpreted as having any dependency or requirement relating to any oneor combination of the components illustrated in the embodiments of thecomputer system 800.

The computer system 800 may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): a keyboard 801, a mouse 802, a trackpad 803, atouchscreen 810, a joystick 805, a microphone 806, a scanner 807, and acamera 808.

The computer system 800 may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouchscreen 810 or the joystick 805, but there can also be tactilefeedback devices that do not serve as input devices), audio outputdevices (such as: speakers 809, headphones (not depicted)), visualoutput devices (such as screens 810 to include cathode ray tube (CRT)screens, liquid-crystal display (LCD) screens, plasma screens, organiclight-emitting diode (OLED) screens, each with or without touchscreeninput capability, each with or without tactile feedback capability—someof which may be capable to output two dimensional visual output or morethan three dimensional output through means such as stereographicoutput; virtual-reality glasses (not depicted), holographic displays andsmoke tanks (not depicted)), and printers (not depicted). A graphicsadapter 850 generates and outputs images to the touchscreen 810.

The computer system 800 can also include human accessible storagedevices and their associated media such as optical media including aCD/DVD ROM/RW drive 820 with CD/DVD or the like media 821, a thumb drive822, a removable hard drive or solid state drive 823, legacy magneticmedia such as tape and floppy disc (not depicted), specializedROM/ASIC/PLD based devices such as security dongles (not depicted), andthe like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

The computer system 800 can also include interface(s) to one or morecommunication networks 855. The communication networks 855 can forexample be wireless, wireline, optical. The networks 855 can further belocal, wide-area, metropolitan, vehicular and industrial, real-time,delay-tolerant, and so on. Examples of the networks 855 include localarea networks such as Ethernet, wireless LANs, cellular networks toinclude global systems for mobile communications (GSM), third generation(3G), fourth generation (4G), fifth generation (5G), Long-Term Evolution(LTE), and the like, TV wireline or wireless wide area digital networksto include cable TV, satellite TV, and terrestrial broadcast TV,vehicular and industrial to include CANBus, and so forth. The networks855 commonly require external network interface adapters that attachedto certain general purpose data ports or peripheral buses 849 (such as,for example universal serial bus (USB) ports of the computer system 800;others are commonly integrated into the core of the computer system 800by attachment to a system bus as described below, for example, a networkinterface 854 including an Ethernet interface into a PC computer systemand/or a cellular network interface into a smartphone computer system.Using any of these networks 855, the computer system 800 can communicatewith other entities. Such communication can be uni-directional, receiveonly (for example, broadcast TV), uni-directional send-only (for exampleCANbus to certain CANbus devices), or bi-directional, for example toother computer systems using local or wide area digital networks.Certain protocols and protocol stacks can be used on each of thosenetworks 855 and network interfaces 854 as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces 854 can be attached to a core 840 of thecomputer system 800.

The core 840 can include one or more Central Processing Units (CPU) 841,Graphics Processing Units (GPU) 842, specialized programmable processingunits in the form of Field Programmable Gate Areas (FPGA) 843, hardwareaccelerators 844 for certain tasks, and so forth. These devices, alongwith read-only memory (ROM) 845, random-access memory (RAM) 846,internal mass storage 847 such as internal non-user accessible harddrives, solid-state drives (SSDs), and the like, may be connectedthrough a system bus 848. In some computer systems, the system bus 848can be accessible in the form of one or more physical plugs to enableextensions by additional CPUs, GPU, and the like. The peripheral devicescan be attached either directly to the core's system bus 848, or throughthe peripheral buses 849. Architectures for a peripheral bus includeperipheral component interconnect (PCI), USB, and the like.

The CPUs 841, GPUs 842, FPGAs 843, and hardware accelerators 844 canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in theROM 845 or RAM 846. Transitional data can also be stored in the RAM 846,whereas permanent data can be stored for example, in the internal massstorage 847. Fast storage and retrieve to any of the memory devices canbe enabled through the use of cache memory, that can be closelyassociated with the CPU 841, GPU 842, internal mass storage 847, ROM845, RAM 846, and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of embodiments, or they can be of the kind well known andavailable to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system 800having architecture, and specifically the core 840 can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core 840 that are of non-transitorynature, such as the core-internal mass storage 847 or ROM 845. Thesoftware implementing various embodiments can be stored in such devicesand executed by the core 840. A computer-readable medium can include oneor more memory devices or chips, according to particular needs. Thesoftware can cause the core 840 and specifically the processors therein(including CPU, GPU, FPGA, and the like) to execute particular processesor particular parts of particular processes described herein, includingdefining data structures stored in the RAM 846 and modifying such datastructures according to the processes defined by the software. Inaddition or as an alternative, the computer system can providefunctionality as a result of logic hardwired or otherwise embodied in acircuit (for example: the hardware accelerator 844), which can operatein place of or together with software to execute particular processes orparticular parts of particular processes described herein. Reference tosoftware can encompass logic, and vice versa, where appropriate.Reference to a computer-readable media can encompass a circuit (such asan integrated circuit (IC)) storing software for execution, a circuitembodying logic for execution, or both, where appropriate. Embodimentsencompass any suitable combination of hardware and software.

While this disclosure has described several embodiments, there arealterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods that, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

1. A method of interframe point cloud attribute coding, the method beingperformed by at least one processor, and the method comprising:obtaining, as a motion estimation unreliability measure of motionestimation of a target frame, a value inversely proportional to a ratioof a number of first point cloud samples of the target framerespectively with second point cloud samples of an interframe referenceframe, to a number of point cloud samples in the target frame;identifying whether the obtained motion estimation unreliability measureis greater than a predetermined threshold; based on the obtained motionestimation unreliability measure being identified to be greater than thepredetermined threshold, skipping motion compensation of the targetframe; and based on the obtained motion estimation unreliability measurebeing identified to be less than or equal to the predeterminedthreshold, performing the motion compensation of the target frame. 2.The method of claim 1, wherein a lesser value of the motion estimationunreliability measure indicates a greater uncertainty of the motionestimation, and a greater value of the motion estimation unreliabilitymeasure indicates a lesser uncertainty of the motion estimation.
 3. Themethod of claim 1, further comprising obtaining the second point cloudsamples, using a nearest-neighbor search algorithm.
 4. The method ofclaim 1, further comprising, based on the obtained motion estimationunreliability measure being identified to be greater than thepredetermined threshold, skipping prediction of an attribute of a pointincluded in one among the point cloud samples in the target frame. 5.The method of claim 1, further comprising performing prediction of anattribute of a point included in one among the point cloud samples inthe target frame of which the motion compensation is performed.
 6. Themethod of claim 5, wherein the attribute comprises either one or both ofa color value and a reflectance value of the point.
 7. The method ofclaim 5, wherein the performing the prediction comprises applying apredicting transform or a lifting transform to the attribute.
 8. Anapparatus for interframe point cloud attribute coding, the apparatuscomprising: at least one memory configured to store computer programcode; and at least one processor configured to access the at least onememory and operate according to the computer program code, the computerprogram code comprising: obtaining code configured to cause the at leastone processor to obtain, as a motion estimation unreliability measure ofmotion estimation of a target frame, a value inversely proportional to aratio of a number of first point cloud samples of the target framerespectively with second point cloud samples of an interframe referenceframe, to a number of point cloud samples in the target frame;identifying code configured to cause the at least one processor toidentify whether the obtained motion estimation unreliability measure isgreater than a predetermined threshold; skipping code configured tocause the at least one processor to, based on the obtained motionestimation unreliability measure being identified to be greater than thepredetermined threshold, skip motion compensation of the target frame;and performing code configured to cause the at least one processor to,based on the obtained motion estimation unreliability measure beingidentified to be less than or equal to the predetermined threshold,perform the motion compensation of the target frame.
 9. The apparatus ofclaim 8, wherein a lesser value of the motion estimation unreliabilitymeasure indicates a greater uncertainty of the motion estimation, and agreater value of the motion estimation unreliability measure indicates alesser uncertainty of the motion estimation.
 10. The apparatus of claim8, wherein the obtaining code is further configured to cause the atleast one processor to obtain the second point cloud samples, using anearest-neighbor search algorithm.
 11. The apparatus of claim 8, whereinthe skipping code is further configured to cause the at least oneprocessor to, based on the obtained motion estimation unreliabilitymeasure being identified to be greater than the predetermined threshold,skip prediction of an attribute of a point included in one among thepoint cloud samples in the target frame.
 12. The apparatus of claim 8,wherein the performing code is further configured to cause the at leastone processor to perform prediction of an attribute of a point includedin one among the point cloud samples in the target frame of which themotion compensation is performed.
 13. The apparatus of claim 12, whereinthe attribute comprises either one or both of a color value and areflectance value of the point.
 14. The apparatus of claim 12, whereinthe performing code is further configured to cause the at least oneprocessor to apply a predicting transform or a lifting transform to theattribute.
 15. A non-transitory computer-readable storage medium storinginstructions that cause at least one processor to: obtain, as a motionestimation unreliability measure of motion estimation of a target frame,a value inversely proportional to a ratio of a number of first pointcloud samples of the target frame respectively with second point cloudsamples of an interframe reference frame, to a number of point cloudsamples in the target frame; identify whether the obtained motionestimation unreliability measure is greater than a predeterminedthreshold; based on the obtained motion estimation unreliability measurebeing identified to be greater than the predetermined threshold, skipmotion compensation of the target frame; and based on the obtainedmotion estimation unreliability measure being identified to be less thanor equal to the predetermined threshold, perform the motion compensationof the target frame.
 16. The non-transitory computer-readable storagemedium of claim 15, wherein a lesser value of the motion estimationunreliability measure indicates a greater uncertainty of the motionestimation, and a greater value of the motion estimation unreliabilitymeasure indicates a lesser uncertainty of the motion estimation.
 17. Thenon-transitory computer-readable storage medium of claim 15, wherein theinstructions further cause the at least one processor to obtain thesecond point cloud samples, using a nearest-neighbor search algorithm.18. The non-transitory computer-readable storage medium of claim 15,wherein the instructions further cause the at least one processor to,based on the obtained motion estimation unreliability measure beingidentified to be greater than the predetermined threshold, skipprediction of an attribute of a point included in one among the pointcloud samples in the target frame.
 19. The non-transitorycomputer-readable storage medium of claim 15, wherein the instructionsfurther cause the at least one processor to perform prediction of anattribute of a point included in one among the point cloud samples inthe target frame of which the motion compensation is performed.
 20. Thenon-transitory computer-readable storage medium of claim 19, wherein theattribute comprises either one or both of a color value and areflectance value of the point.